AI Article Synopsis
- The study focused on Wuqi County in Shaanxi Province, where ongoing vegetation recovery measures were analyzed through 100 loess soil samples from 24 profiles.
- The research involved analyzing total nitrogen (TN) and alkali hydrolysable nitrogen (AHN) contents using visible/near-infrared (VNIR) spectroscopy, leading to the development of calibration models for predicting these nitrogen levels.
- Results showed that the optimal model for predicting TN utilized first derivative reflectance, while the AHN model, which requires more research, provided only rough predictions of nitrogen content.
Article Abstract
Wuqi County of Shaanxi Province, where the vegetation recovering measures have been carried out for years, was taken as the study area. A total of 100 loess samples from 24 different profiles were collected. Total nitrogen (TN) and alkali hydrolysable nitrogen (AHN) contents of the soil samples were analyzed, and the soil samples were scanned in the visible/near-infrared (VNIR) region of 350-2500 nm in the laboratory. The calibration models were developed between TN and AHN contents and VNIR values based on correlation analysis (CA) and partial least squares regression (PLS). Independent samples validated the calibration models. The results indicated that the optimum model for predicting TN of loess was established by using first derivative of reflectance. The best model for predicting AHN of loess was established by using normal derivative spectra. The optimum TN model could effectively predict TN in loess from 0 to 40 cm, but the optimum AHN model could only roughly predict AHN at the same depth. This study provided a good method for rapidly predicting TN of loess where vegetation recovering measures have been adopted, but prediction of AHN needs to be further studied.
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